It is not yet clear how automation will reshape the industrial landscape – but we do know that smart technologies such as Artificial Intelligence, blockchain, big data, cloud computing, hyperconnectivity, 3D/ 4D printing, smart materials, and synthetic biology are developing at an exponential rate. Article by Rohit Talwar, Steve Wells and Alexandra Whittington, Fast Future

Individually and when combined, they will have an impact from automated warehouses and autonomous cars to computerised drug discovery and the diagnosis of Alzheimer’s disease ten years before the symptoms show – it’s already happening.

However, we don’t know how far and how fast AI and these other disruptive technologies will spread. Furthermore, we don’t know how many jobs they will take out, we don’t know how society will respond, we don’t know the extent to which firms will retain people when they automate, we don’t know how fast the new sectors will grow, and we don’t know how many new jobs they will create. In practice we are pretty clueless. Which is why we need to start thinking about the problems – and the possible solutions – now.

The UK’s position:
Britain’s ruling Conservative Party is loath to acknowledge the possibility of rising unemployment due to automation. The hope is that encouragement of free markets and lower corporate tax rates will drive business growth and employment. They believe that unemployment costs will be met through revenues from corporate and individual taxes coupled with VAT.

In contrast, the rising number of young members in the opposition Labour Party are concerned about the impact on their future – spurred on by already high levels of youth and graduate unemployment. They are keen to ensure Britain doesn’t go into the kind of decline we saw with Greece and Spain.

In response, and acknowledging the fundamental changes taking place in the industrial economy, Labour has been mooting the idea of “robot taxes” to finance the cost of adult retraining, education transformation and unemployment provisions. The argument is that robots should be taxed because they will be considered as something that creates value for the owner, like property, and if firms are cutting headcounts, then they are likely to be making higher profits

Furthermore, the belief is that those who will receive the benefits will spend that money with the firms who paid the robot taxes.

What might robot taxes pay for?
Clearly, the primary purpose should be to address the societal consequences of job automation. So, the most obvious application would be to fund unemployment benefits or guaranteed incomes and services.

However, it is difficult to believe that any tax raised could be permanently and transparently ring-fenced by government for one use or another. When one looks across the range of taxes now, is there evidence to show that fuel duty is ploughed back into highway maintenance, for example?

Alongside unemployment costs, there is a strong argument that a significant proportion of the revenue from robot taxes should be channelled directly into public education.  This would create a positive role for robots in society, which would be to pay for public schools and universities.  The hope is that this would prevent a backlash from the people whose jobs are lost to automation, and create enough money to revamp an outdated education system into a forward looking one that teaches the knowledge and skills which will be in demand in 2030 and beyond, when most jobs as we now know them are absorbed by robots and algorithms.

A robot tax could help pay for a new approach to education which develops the whole person, not just the ‘future worker’.  These would include life skills (cooking, health and household management), interpersonal skills (listening, leadership, writing) and self-awareness (mindfulness, meditation, mental health strategies).  The underlying principle is that we should use the value of automation to benefit society and prevent future problems.

What is the likelihood of a robot tax?
Some governments have started to think about the spending side of the equation – the human consequences of automation – exploring everything from new approaches to adult education to encouraging the creation of start-ups.

A number such as Canada, Finland and Germany have also been experimenting with different forms of guaranteed or universal basic income (UBI). These are relatively small experiments – the intention is to learn about them before they are required. The experiments are looking at different funding models, whether any access conditions should be applied, and the impact on mental health, domestic violence, crime, and community cohesion. Such experimentation seems eminently sensible as an input to any nation’s debate on the topic.

At a broader tax policy level, across the world, rapid automation must be seen as one very important driver of change to nations’ tax collection regimes. Clearly the public spending policy decisions of these governments will also have an impact. Hence it becomes critical to explore different possible scenarios to understand the likely spending requirements and revenues under a range of different conditions. Governments can then examine both their spending priorities and possible revenue instruments. As such, it may be that the impact of automation plays a much bigger role in driving future decisions around taxation policy, broadening the debate beyond the deployment and taxation of robots.

Who might lead the way?
It seems unlikely that any government would introduce these kinds of measures within the next 2-5 years, but by 2030 the possible pace of change means they could well be commonplace in many industrial nations. Countries that are embracing automation and the digital era in all its forms such as South Korea, Japan and Singapore might be among the first to implement some form of automation taxation mechanism.

Whilst China is saying little right now it has the capacity to enact policy rapidly should the need arise. In India, the overt and hidden political power of the super-corporates means the country would be a very late adopter. In Europe, nations such as Estonia, Finland, Sweden, Denmark, Iceland, and Germany are likely to be among the first to revamp their tax systems in this way.

In Silicon Valley many argue in favour of robot taxes, yet the US is likely to face strong resistance to such changes. Indeed, it could well be among the last to go down this route and might conceivably not do so at all without a fundamental change in its governance and electoral systems.

How might these taxes work?
The going in point here should be to evolve a more flexible approach to creating income to fund future public services. The basis of corporate taxation could become even more complex with systems applying AI to large multi-variable data sets to establish a tax liability based on the sector, revenues / profits per employee, the number of people employed, and geographic location. The algorithms could also take account factors such as expenditure on training and retraining current and former employees, the support given by firms to start-ups, the level of employment created further down the value chain, and the amount of tax paid by the firm’s employees.

Perhaps evaluation of a business’s broader impact on society could also factor into the level of taxation applied to its profits – such as the actual level of human employment, local and national social responsibility, environmental impact – so that tax paid is based on the outcomes of a business’s operation across a range of different domains.

Some measure of net added value could also be considered. For example, a firm may train its employees so well that they go on to higher paying jobs elsewhere or to generate employment and tax revenues by starting their own business. How might their taxation be assessed relative to a firm who invests little in people development and whose staff cannot find jobs elsewhere when made redundant. In the UK, the PAYE system is a government mechanism by which employers collect tax from employees and transfer it to the tax authorities. This could be used to calculate credits for application against a business’s corporation tax liability.

An interesting scenario to explore would be the possibility that AI could create the opportunity for governments to recover public spending commitments pro-rata from every tax payer and corporation in the country – purely based on individual incomes or business revenues. In the worst-case scenario this could mean firms posting a loss because they failed to make a profit after paying their fair share for running the country.

The key here is modelling a variety of different approaches to see which produces the fairest and most transparent system. This may well evolve over time as the controlling AI algorithm learns about what behaviours it engenders in firms to try and reduce their tax bill.

There are however precedents we can learn from. For example, the British pharmaceutical industry has paid a levy based on revenue or capital employed on its supplies to the NHS with a series of allowable and dis-allowable expenses. Whilst this approach has been designed as a mechanism to control profits on medicine supplies to the NHS (while seeking to reward investment in R&D) a similar approach could be taken with companies and the level of automation they employ, versus their “investment” in people.

AI is creating the tools that are driving the pace of automation and the likely prospect of increased unemployment. Equally, AI tools could also be used to design and develop new approaches to taxation that could help us address the societal consequences of technological disruption and ensure a very human future for all.

www.fastfuture.com